pH and Eh are related, but not entirely dependent on each other. Eh is only related to pH for reactions involving protons or hydroxide, because these affect pH. The more energy-generating reactions are occuring, the more negative we expect the Eh to be (More negative = more reduced). For example the TCA cycle creates NADH and FADH2 which are both reduced by gaining e- with H.
As mentioned, when carbon dioxide is dissolved in water, the release of hydrogen ions in carbonic acid is what lowers the pH. As CO2 levels increase around Earth, the amount of dissolved CO2 also increases, which increases the amount of carbonic acid, therefore decreasing the pH
CO2 (aq) + H2O ⇌ H2CO3 (aq) Carbon dioxide + Water ⇌ Carbonic Acid
Henry’s Law of the Solubility of Gases Henry’s Law is a law of physics formulated by William Henry in 1803. It states:
“At a constant temperature, the amount of a given gas that dissolves in a given type and volume of liquid is directly proportional to the partial pressure of that gas in equilibrium with that liquid.” An equivalent way of stating the law is that the solubility of a gas in a liquid is directly proportional to the partial pressure of the gas above the liquid.” (emphasis added)3
https://pubmed.ncbi.nlm.nih.gov/22700881/#&gid=article-figures&pid=figure-1-uid-0 https://blog.orendatech.com/co2-and-ph-henrys-law
# Load packages-----------------------------------------------------------------
library(readxl)
library(tidyverse)
library(lubridate)
library(lmerTest)
library(lme4)
library(broom.mixed)
library(emmeans)
library(ggplot2)
library(afex)
library(simputation)
library(plotly)
library(MAW636)
afex::set_sum_contrasts()
theme_set(theme_bw())
#—————————————————————
# treatment schedule
trtsched_upload <- read_excel("00_input/MAW636 Treatment Schedule.xlsx") %>%
pivot_longer(cols = -Cow, names_to = "period", values_to = "trt") %>%
rename(cow = "Cow") %>%
mutate(cow = as.character(cow)) %>%
na_if("NA") %>%
drop_na() %>%
separate(trt, c("osc_lvl", "prot_lvl"), sep = cumsum(c(1, 2))) %>%
mutate(trt = paste0(osc_lvl, prot_lvl)) # separate into factorial trt
#—————————————————————
rumen <- read_xlsx("00_input/MAW636 GD Data Upload.xlsx", sheet = "rumen_ph") %>%
mutate(day = factor(day, levels = c("Day1", "Day2", "Day3", "Day4"))) %>%
mutate(day_n = as.numeric(day)) %>%
mutate(time_n = (day_n*24-32)+hr) %>%
mutate(cow = as.character(cow)) %>%
mutate(rumen_ph = as.numeric(rumen_ph)) %>%
left_join(trtsched_upload) %>%
filter(rumen_ph >4 & rumen_ph<8)## remove outliers
csv_out(rumen,"org")logger <- read_excel("00_input/Logger Data/MAW636 combined logger data.xlsx") %>%
mutate(pH = as.numeric(pH),
cow = as.character(cow)) %>%
mutate(date2 = as_date(date)) %>%
filter(date2!= "2021-07-24") %>%
filter(ymd_hms(date) <= ymd_hms("2021-08-06 17:00:00")) %>%
mutate(ct = cut(date, breaks = "5 min")) %>%
left_join(trtsched_upload %>% filter(period == "P4")) ## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion## Joining, by = "cow"logger_o <- logger %>%
filter(osc_lvl == "O")
logger_s <- logger %>%
filter(osc_lvl == "S")
logger_sum_o <- logger_o %>%
group_by(ct) %>%
mutate(date3 = min(date)) %>%
group_by(ct, date3, date2, cow) %>%
summarise(ph_sum = mean(pH)) %>%
ungroup() %>%
#mutate(hr = hour(date3)) %>%
mutate(osc_day = case_when(
date2 == "2021-07-24"| date2 == "2021-07-25" ~ "Low",
date2 == "2021-07-26"| date2 == "2021-07-27" ~ "High",
date2 == "2021-07-28"| date2 == "2021-07-29" ~ "Low",
date2 == "2021-07-30"| date2 == "2021-07-31" ~ "High",
date2 == "2021-08-01"| date2 == "2021-08-02" ~ "Low",
date2 == "2021-08-03"| date2 == "2021-08-04" ~ "High",
date2 == "2021-08-05"| date2 == "2021-08-06" ~ "Low")) #%>% ## `summarise()` has grouped output by 'ct', 'date3', 'date2'. You can override
## using the `.groups` argument. #mutate(day = cut(date2))
logger_sum_s <- logger_s %>%
group_by(ct) %>%
mutate(date3 = min(date)) %>%
group_by(ct, date3, date2, cow) %>%
summarise(ph_sum = mean(pH)) %>%
ungroup() %>%
#mutate(hr = hour(date3)) %>%
mutate(osc_day = case_when(
date2 == "2021-07-24"| date2 == "2021-07-25" ~ "Low",
date2 == "2021-07-26"| date2 == "2021-07-27" ~ "High",
date2 == "2021-07-28"| date2 == "2021-07-29" ~ "Low",
date2 == "2021-07-30"| date2 == "2021-07-31" ~ "High",
date2 == "2021-08-01"| date2 == "2021-08-02" ~ "Low",
date2 == "2021-08-03"| date2 == "2021-08-04" ~ "High",
date2 == "2021-08-05"| date2 == "2021-08-06" ~ "Low")) #%>% ## `summarise()` has grouped output by 'ct', 'date3', 'date2'. You can override
## using the `.groups` argument. #mutate(day = cut(date2))
g2 = logger_sum_o %>%
group_by(cow) %>%
plot_ly(., x = ~date3, y = ~ph_sum, color = ~cow, type = "scatter", mode = "lines")
g2%>%
layout(.,
title='OSCILLATING 5-min. rolling average of rumen pH from indwelling loggers',
xaxis = list(rangeslider = list(visible = T)),
shapes = list(
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35,
x0 = as_datetime("2021-07-25 00:05:09"),
x1 = as_datetime("2021-07-26 08:05:09")
),
list(
type = "rect",
fillcolor = "grey", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-07-26 08:05:09"),
x1 = as_datetime("2021-07-28 08:05:09")
),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-07-28 08:00:09"),
x1 = as_datetime("2021-07-30 08:05:09")
),
list(
type = "rect",
fillcolor = "grey", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-07-30 08:05:09"),
x1 = as_datetime("2021-08-01 08:05:09")
),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-01 08:05:09"),
x1 = as_datetime("2021-08-03 08:05:09")
),
list(
type = "rect",
fillcolor = "grey", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-03 08:05:09"),
x1 = as_datetime("2021-08-05 08:05:09")
),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-05 08:05:09"),
x1 = as_datetime("2021-08-06 17:05:09")),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-05 08:05:09"),
x1 = as_datetime("2021-08-06 17:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-25 08:05:09"),
x1 = as_datetime("2021-07-25 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-26 08:05:09"),
x1 = as_datetime("2021-07-26 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-27 08:05:09"),
x1 = as_datetime("2021-07-27 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-28 08:05:09"),
x1 = as_datetime("2021-07-28 08:05:09")
),
list(
type = "line", dash = "dash",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-29 08:05:09"),
x1 = as_datetime("2021-07-29 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-30 08:05:09"),
x1 = as_datetime("2021-07-30 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-31 08:05:09"),
x1 = as_datetime("2021-07-31 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-01 08:05:09"),
x1 = as_datetime("2021-08-01 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-02 08:05:09"),
x1 = as_datetime("2021-08-02 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-03 08:05:09"),
x1 = as_datetime("2021-08-03 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-04 08:05:09"),
x1 = as_datetime("2021-08-04 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-05 08:05:09"),
x1 = as_datetime("2021-08-05 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-06 08:05:09"),
x1 = as_datetime("2021-08-06 08:05:09")
)))g3 = logger_sum_s %>%
group_by(cow) %>%
plot_ly(., x = ~date3, y = ~ph_sum, color = ~cow, type = "scatter", mode = "lines")
g3 %>%
layout(.,
title = "STATIC 5-min. rolling average of rumen pH from indwelling loggers",
xaxis = list(rangeslider = list(visible = T)),
shapes = list(
list(
arrow = 1,
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35,
x0 = as_datetime("2021-07-25 00:05:09"),
x1 = as_datetime("2021-07-26 08:05:09")
),
list(
type = "rect",
fillcolor = "grey", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-07-26 08:05:09"),
x1 = as_datetime("2021-07-28 08:05:09")
),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-07-28 08:00:09"),
x1 = as_datetime("2021-07-30 08:05:09")
),
list(
type = "rect",
fillcolor = "grey", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-07-30 08:05:09"),
x1 = as_datetime("2021-08-01 08:05:09")
),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-01 08:05:09"),
x1 = as_datetime("2021-08-03 08:05:09")
),
list(
type = "rect",
fillcolor = "grey", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-03 08:05:09"),
x1 = as_datetime("2021-08-05 08:05:09")
),
list(
type = "rect",
fillcolor = "white", opacity = 0.1,
y0 = 5, y1 = 7.35, line_width = 0,
x0 = as_datetime("2021-08-05 08:05:09"),
x1 = as_datetime("2021-08-06 17:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-25 08:05:09"),
x1 = as_datetime("2021-07-25 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-26 08:05:09"),
x1 = as_datetime("2021-07-26 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-27 08:05:09"),
x1 = as_datetime("2021-07-27 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-28 08:05:09"),
x1 = as_datetime("2021-07-28 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-29 08:05:09"),
x1 = as_datetime("2021-07-29 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-30 08:05:09"),
x1 = as_datetime("2021-07-30 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-07-31 08:05:09"),
x1 = as_datetime("2021-07-31 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-01 08:05:09"),
x1 = as_datetime("2021-08-01 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-02 08:05:09"),
x1 = as_datetime("2021-08-02 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-03 08:05:09"),
x1 = as_datetime("2021-08-03 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-04 08:05:09"),
x1 = as_datetime("2021-08-04 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-05 08:05:09"),
x1 = as_datetime("2021-08-05 08:05:09")
),
list(
type = "line",
fillcolor = "blue", opacity = 1,
y0 = 7, y1 = 7.35,
x0 = as_datetime("2021-08-06 08:05:09"),
x1 = as_datetime("2021-08-06 08:05:09")
)
)
)4.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.1
To be completed
##################################
logger_sum_o <- logger_o %>%
group_by(ct) %>%
mutate(date3 = min(date)) %>%
group_by(ct, date3, date2, cow) %>%
summarise(temp_sum = mean(temp)) %>%
ungroup() %>%
#mutate(hr = hour(date3)) %>%
mutate(osc_day = case_when(
date2 == "2021-07-24"| date2 == "2021-07-25" ~ "Low",
date2 == "2021-07-26"| date2 == "2021-07-27" ~ "High",
date2 == "2021-07-28"| date2 == "2021-07-29" ~ "Low",
date2 == "2021-07-30"| date2 == "2021-07-31" ~ "High",
date2 == "2021-08-01"| date2 == "2021-08-02" ~ "Low",
date2 == "2021-08-03"| date2 == "2021-08-04" ~ "High",
date2 == "2021-08-05"| date2 == "2021-08-06" ~ "Low")) #%>%
#mutate(day = cut(date2))
logger_sum_s <- logger_s %>%
group_by(ct) %>%
mutate(date3 = min(date)) %>%
group_by(ct, date3, date2, cow) %>%
summarise(temp_sum = mean(temp)) %>%
ungroup() %>%
#mutate(hr = hour(date3)) %>%
mutate(osc_day = case_when(
date2 == "2021-07-24"| date2 == "2021-07-25" ~ "Low",
date2 == "2021-07-26"| date2 == "2021-07-27" ~ "High",
date2 == "2021-07-28"| date2 == "2021-07-29" ~ "Low",
date2 == "2021-07-30"| date2 == "2021-07-31" ~ "High",
date2 == "2021-08-01"| date2 == "2021-08-02" ~ "Low",
date2 == "2021-08-03"| date2 == "2021-08-04" ~ "High",
date2 == "2021-08-05"| date2 == "2021-08-06" ~ "Low")) #%>%
#mutate(day = cut(date2))
g3 = logger_sum_s %>%
group_by(cow) %>%
plot_ly(., x = ~date3, y = ~temp_sum, color = ~cow, type = "scatter", mode = "lines")
g3 %>%
layout(.,
title = "STATIC 5-min. rolling average of rumen temperature from indwelling loggers",
xaxis = list(rangeslider = list(visible = T))
)logger_sum_o <- logger_o %>%
group_by(ct) %>%
mutate(date3 = min(date)) %>%
group_by(ct, date3, date2, cow) %>%
summarise(mv_sum = mean(as.numeric(mv))) %>%
ungroup() %>%
#mutate(hr = hour(date3)) %>%
mutate(osc_day = case_when(
date2 == "2021-07-24"| date2 == "2021-07-25" ~ "Low",
date2 == "2021-07-26"| date2 == "2021-07-27" ~ "High",
date2 == "2021-07-28"| date2 == "2021-07-29" ~ "Low",
date2 == "2021-07-30"| date2 == "2021-07-31" ~ "High",
date2 == "2021-08-01"| date2 == "2021-08-02" ~ "Low",
date2 == "2021-08-03"| date2 == "2021-08-04" ~ "High",
date2 == "2021-08-05"| date2 == "2021-08-06" ~ "Low")) #%>%
#mutate(day = cut(date2))
logger_sum_s <- logger_s %>%
group_by(ct) %>%
mutate(date3 = min(date)) %>%
group_by(ct, date3, date2, cow) %>%
summarise(mv_sum = mean(as.numeric(mv))) %>%
ungroup() %>%
#mutate(hr = hour(date3)) %>%
mutate(osc_day = case_when(
date2 == "2021-07-24"| date2 == "2021-07-25" ~ "Low",
date2 == "2021-07-26"| date2 == "2021-07-27" ~ "High",
date2 == "2021-07-28"| date2 == "2021-07-29" ~ "Low",
date2 == "2021-07-30"| date2 == "2021-07-31" ~ "High",
date2 == "2021-08-01"| date2 == "2021-08-02" ~ "Low",
date2 == "2021-08-03"| date2 == "2021-08-04" ~ "High",
date2 == "2021-08-05"| date2 == "2021-08-06" ~ "Low")) #%>%
#mutate(day = cut(date2))
g3 = logger_sum_s %>%
group_by(cow) %>%
plot_ly(., x = ~date3, y = ~mv_sum, color = ~cow, type = "scatter", mode = "lines")
g3 %>%
layout(.,
title = "STATIC 5-min. rolling average of rumen redox potential from indwelling loggers",
xaxis = list(rangeslider = list(visible = T))
)7/24/21 7/25/21 Low 7/26/21 7/27/21 High 7/28/21 7/29/21 Low 7/30/21 7/31/21 High 8/1/21 8/2/21 Low 8/3/21 8/4/21 High 8/5/21 8/6/21 Low 8/7/21 8/8/21 High
g2 %>%
layout(., shapes = lines)
layout(showlegend = F, title='Time Series with Rangeslider',
xaxis = list(rangeslider = list(visible = T)))
p1 <- logger %>%
ggplot(aes(x = date, y = pH, group = cow, col = cow)) + geom_point(size = .2)
ggplotly(p1)
p2 <- logger_sum_o %>%
ggplot(aes(x = as_datetime(date3), y = ph_sum, group = cow, col = cow)) +
geom_point(size = .2) + geom_line() + scale_x_datetime(date_breaks = "1 day") +
theme(axis.text.x = element_text(angle = 90)) +
geom_rect(aes(xmin = date3, xmax = (date3)+300,
ymin = -Inf, ymax = Inf, fill = osc_day), color = "NA", alpha = .02)
p2
as_datetime(logger_sum$date3)[1]
as_datetime(logger_sum$date3)[10000]
g2 = ggplotly(p2)
case_when(hr = 8)
seq(1, 10, 1)
?seq
line <- list(
type = "line",
line = list(color = "pink"),
xref = "date3",
yref = "ph_sum"
)
lines <- list()
for (i in seq(1,10, 1)) {
line[["x0"]] <- as_datetime("2021-07-25 00:05:09")
line[["x1"]] <- as_datetime("2021-07-25 00:05:09") + i
line[["y0"]] <- 5
line[["y1"]] <- 7.35
lines<- c(lines, list(line))
}
list(type = "line", linetype = "dashed",
y0 = 5, y1 = 7.35,
x0 = as_datetime("2021-07-25 00:05:09"),
x1 = as_datetime("2021-07-25 00:05:09")),
list(type = "line", dash = "dash",
y0 = 5, y1 = 7.35,
x0 = as_datetime("2021-07-25 00:05:09"),
x1 = as_datetime("2021-07-25 00:05:09")),